class_docstr: Abstract clusterer which takes tokens and maps them into a vector space.
  Optionally performs singular value decomposition to reduce the
  dimensionality.
code: def classify(self, vector):
      if self._should_normalise:
          vector = self._normalise(vector)
      if self._Tt is not None:
          vector = numpy.dot(self._Tt, vector)
      cluster = self.classify_vectorspace(vector)
      return self.cluster_name(cluster)
  
imports: import copy
  from abc import abstractmethod
  from math import sqrt
  from sys import stdout
  from nltk.cluster.api import ClusterI
label_desc: Use image node_id 4 for calling the VectorSpaceClusterer obj's underlying member method code with example usage: obj.classify(vector) and returns: self